Course Description This course is part three of a specialization on algorithms and data structures. It covers basic algorithm design techniques such as divide and conquer, dynamic programming, and ...

Formulate linear and integer programming problems for solving commonly encountered optimization problems. Understand how approximation algorithms compute solutions that are guaranteed to be within ...

Dynamic programming (DP) algorithms have become indispensable in computational biology, addressing problems that range from sequence alignment and phylogenetic inference to RNA secondary structure ...

This is an advanced undergraduate course on algorithms. This course examines such topics as greedy algorithms, dynamic programming, graph algorithms, string processing, and algorithms for ...

Introduction to theory of algorithms and basics of Python programming. Algorithmic thinking: Do you know how to multiply integers? Basic toolkit for the design and analysis of algorithms, and an ...

Successive Linear Programming (SLP) algorithms solve nonlinear optimization problems via a sequence of linear programs. They have been widely used, particularly in the oil and chemical industries, ...

Introduction to theory of algorithms and basics of Python programming. Algorithmic thinking: Do you know how to multiply integers? Basic toolkit for the design and analysis of algorithms, and an ...

Jonathan Eckstein, Nonlinear Proximal Point Algorithms Using Bregman Functions, with Applications to Convex Programming, Mathematics of Operations Research, Vol. 18, No. 1 (Feb., 1993), pp. 202-226 ...

Sequence alignment methods often use something called a 'dynamic programming' algorithm. What is dynamic programming and how does it work?